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Nonparametric inference for reversed mean models with panel count data.

L I Liu1, Wen Su2, Guosheng Yin2

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Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing recurrent event data, especially when the data is cut short by a terminal event. The proposed method accurately estimates event rates near the end of observation periods.

Keywords:
Nonparametric testsrecurrent eventsreversed mean modelterminal event

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Longitudinal Data Analysis

Background:

  • Panel count data involves recurrent events observed at discrete time points.
  • Recurrent event processes can be truncated by informative terminal events.
  • Understanding event behavior near the terminal event is crucial.

Purpose of the Study:

  • To propose a novel statistical framework for analyzing recurrent event data truncated by informative terminal events.
  • To develop a robust method for estimating the mean function of recurrent events, particularly near the terminal event.
  • To establish the theoretical properties and practical utility of the proposed methods.

Main Methods:

  • A reversed mean model is proposed for estimating the mean function of the recurrent event process.
  • A two-stage sieve likelihood-based method is developed to overcome computational challenges with nuisance parameters.
  • General weak convergence theory for M-estimators with nuisance functional parameters is established and applied.

Main Results:

  • The consistency and convergence rate of the two-stage estimator are theoretically established.
  • Asymptotic normality of the proposed estimator is derived using the developed weak convergence theory.
  • A class of two-sample tests is developed for comparing recurrent event processes.

Conclusions:

  • The proposed two-stage sieve likelihood method provides a computationally feasible and statistically sound approach for analyzing truncated recurrent event data.
  • The developed methods demonstrate good performance in simulation studies and are applicable to real-world panel count data.
  • This research contributes to the statistical methodology for handling complex event data in longitudinal studies.